An Exploratory Study on How Democratic School Management Practices Affect the Dynamics of Violence in Schools
Bibliographic record
Abstract
The goal of teachers, schools and support staff is the optimal development of learners. This goal depends on effective school management, which in turn depends on an adequate curriculum and supporting teaching and learning environments. This paper is focused through a literature review on the dynamics of violence in South African schools and the need for democratic school management. The necessity of emotional intelligence teaching in the classroom and learner-centred teaching is shown. This study also investigated the coordinated and integrated management of positive learner behaviour, whole school development and management of a culture of positive behaviour. The establishment of a safe school depends on the management's interaction with the larger social and organisational context in which they find themselves. The study concludes that there is a correlation between Emotional Intelligence teaching methods and learners' behaviour. The use of Emotional Intelligence in the classroom can be regarded as the corrective aspect of discipline, thus, preventing inappropriate behaviour of learners. Recommendations and guidelines for the use of effective adjusted and alternative teaching methods to support school management in the use of emotional skills in the school context are provided.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".